#library to work with data
import pandas as pd

#Library to plot matrices
import numpy as np
import matplotlib . pyplot as plt
import seaborn as sns ; sns . set()
import plotly.express as px
#import geopandas as gpd
import folium
from plotly.offline import plot
import plotly.graph_objects as go
%matplotlib inline
%matplotlib notebook
from IPython.display import Latex
#plotly.offline.init_notebook_mode(connected=True)
#plotly.offline.(i)plot
import plotly.io as pio
pio.renderers.default='notebook'

#Load Data
confirmed = pd. read_csv("G:/My Drive/UPITT/Capstone/Data/covid_confirmed_usafacts.csv")
deaths = pd. read_csv("G:/My Drive/UPITT/Capstone/Data/covid_deaths_usafacts.csv")
vaccine = pd. read_csv("G:/My Drive/UPITT/Capstone/Data/COVID19_CDC_Vaccination_CSV_Download.csv")

#confirmed.head()

#confirmed.tail()

#deaths.tail()

print(confirmed.shape)
print(deaths.shape)
print(vaccine.shape)

(3193, 775)
(3193, 775)
(50209, 14)

#Checking for missing values
#confirmed.isnull().sum()

#Checking for missing values
#deaths.isnull().sum()

Data Manipulation

#Drop Columns
confirmed_df = confirmed.drop(columns=["countyFIPS", "County Name","StateFIPS"])
death_df = deaths.drop(columns=["countyFIPS", "County Name","StateFIPS"])

#Group by State
confirmed_df = confirmed_df.groupby(by="State").aggregate(np.sum).T
death_df = death_df.groupby(by="State").aggregate(np.sum).T

confirmed_df.head()

State	AK	AL	AR	AZ	CA	CO	CT	DC	DE	FL	...	SD	TN	TX	UT	VA	VT	WA	WI	WV	WY
2020-01-22	0	0	0	0	722	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
2020-01-23	0	0	0	0	733	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
2020-01-24	0	0	0	0	739	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
2020-01-25	0	0	0	0	749	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
2020-01-26	0	0	0	1	756	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0

5 rows × 51 columns

death_df.head()

State	AK	AL	AR	AZ	CA	CO	CT	DC	DE	FL	...	SD	TN	TX	UT	VA	VT	WA	WI	WV	WY
2020-01-22	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2020-01-23	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2020-01-24	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2020-01-25	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2020-01-26	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0

5 rows × 51 columns

confirmed_df.index.name='Date'
confirmed_df = confirmed_df.reset_index()

confirmed_df.head()

State	Date	AK	AL	AR	AZ	CA	CO	CT	DC	DE	...	SD	TN	TX	UT	VA	VT	WA	WI	WV	WY
0	2020-01-22	0	0	0	0	722	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
1	2020-01-23	0	0	0	0	733	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
2	2020-01-24	0	0	0	0	739	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
3	2020-01-25	0	0	0	0	749	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
4	2020-01-26	0	0	0	1	756	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0

5 rows × 52 columns

death_df.index.name='Date'
death_df = death_df.reset_index()

death_df.head()

State	Date	AK	AL	AR	AZ	CA	CO	CT	DC	DE	...	SD	TN	TX	UT	VA	VT	WA	WI	WV	WY
0	2020-01-22	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
1	2020-01-23	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2	2020-01-24	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
3	2020-01-25	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
4	2020-01-26	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0

5 rows × 52 columns

Melt

#Change dataframe from wide to long format
confirmed_melt_df = confirmed_df.melt(id_vars='Date').copy()
death_melt_df = death_df.melt(id_vars='Date').copy()

confirmed_melt_df

	Date	State	value
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0
...	...	...	...
39316	2022-02-26	WY	154549
39317	2022-02-27	WY	154549
39318	2022-02-28	WY	154909
39319	2022-03-01	WY	154909
39320	2022-03-02	WY	154909

39321 rows × 3 columns

death_melt_df

	Date	State	value
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0
...	...	...	...
39316	2022-02-26	WY	1718
39317	2022-02-27	WY	1718
39318	2022-02-28	WY	1718
39319	2022-03-01	WY	1741
39320	2022-03-02	WY	1741

39321 rows × 3 columns

Rename

confirmed_melt_df.rename(columns={'value':'Cases'}, inplace=True)
death_melt_df.rename(columns={'value':'Deaths'}, inplace=True)

confirmed_melt_df.head()

	Date	State	Cases
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0

death_melt_df.head()

	Date	State	Deaths
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0

Date

max_date = confirmed_melt_df['Date'].max()
max_date

max_date1 = death_melt_df['Date'].max()
max_date1

'2022-03-02'

confirmed_melt_df['Date'] = pd.to_datetime(confirmed_melt_df['Date'])
death_melt_df['Date'] = pd.to_datetime(death_melt_df['Date'])

confirmed_melt_df.head()

	Date	State	Cases
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0

death_melt_df.head()

	Date	State	Deaths
0	2020-01-22	AK	0
1	2020-01-23	AK	0
2	2020-01-24	AK	0
3	2020-01-25	AK	0
4	2020-01-26	AK	0

confirmed_melt_df['Date'] = confirmed_melt_df['Date'].dt.strftime('%m/%d/%Y')
death_melt_df['Date'] = death_melt_df['Date'].dt.strftime('%m/%d/%Y')

max_date = confirmed_melt_df['Date'].max()
max_date

max_date1 = confirmed_melt_df['Date'].max()
max_date1

'12/31/2021'

Data Visualization

total_confirmed_df = confirmed_melt_df[confirmed_melt_df['Date'] == max_date]
total_death_df = death_melt_df[death_melt_df['Date'] == max_date]

total_confirmed_df

	Date	State	Cases
709	12/31/2021	AK	149907
1480	12/31/2021	AL	896614
2251	12/31/2021	AR	562455
3022	12/31/2021	AZ	1381488
3793	12/31/2021	CA	5397950
4564	12/31/2021	CO	954170
5335	12/31/2021	CT	510188
6106	12/31/2021	DC	94286
6877	12/31/2021	DE	180366
7648	12/31/2021	FL	4222899
8419	12/31/2021	GA	1420034
9190	12/31/2021	HI	112932
9961	12/31/2021	IA	582348
10732	12/31/2021	ID	319382
11503	12/31/2021	IL	2181031
12274	12/31/2021	IN	1250836
13045	12/31/2021	KS	519544
13816	12/31/2021	KY	856145
14587	12/31/2021	LA	828695
15358	12/31/2021	MA	1060110
16129	12/31/2021	MD	726388
16900	12/31/2021	ME	146736
17671	12/31/2021	MI	1710277
18442	12/31/2021	MN	1022212
19213	12/31/2021	MO	1006913
19984	12/31/2021	MS	543737
20755	12/31/2021	MT	197724
21526	12/31/2021	NC	1667493
22297	12/31/2021	ND	173379
23068	12/31/2021	NE	342939
23839	12/31/2021	NH	198667
24610	12/31/2021	NJ	1564253
25381	12/31/2021	NM	350043
26152	12/31/2021	NV	501181
26923	12/31/2021	NY	3469564
27694	12/31/2021	OH	2016095
28465	12/31/2021	OK	708938
29236	12/31/2021	OR	421265
30007	12/31/2021	PA	2036424
30778	12/31/2021	RI	231096
31549	12/31/2021	SC	975320
32320	12/31/2021	SD	178395
33091	12/31/2021	TN	1412302
33862	12/31/2021	TX	4572523
34633	12/31/2021	UT	636992
35404	12/31/2021	VA	1118518
36175	12/31/2021	VT	64447
36946	12/31/2021	WA	849075
37717	12/31/2021	WI	1120663
38488	12/31/2021	WV	328162
39259	12/31/2021	WY	115638

total_death_df

	Date	State	Deaths
709	12/31/2021	AK	938
1480	12/31/2021	AL	16455
2251	12/31/2021	AR	9148
3022	12/31/2021	AZ	24229
3793	12/31/2021	CA	75656
4564	12/31/2021	CO	10322
5335	12/31/2021	CT	9160
6106	12/31/2021	DC	1211
6877	12/31/2021	DE	2286
7648	12/31/2021	FL	62539
8419	12/31/2021	GA	31443
9190	12/31/2021	HI	1090
9961	12/31/2021	IA	7858
10732	12/31/2021	ID	4162
11503	12/31/2021	IL	31017
12274	12/31/2021	IN	19037
13045	12/31/2021	KS	6673
13816	12/31/2021	KY	12118
14587	12/31/2021	LA	14986
15358	12/31/2021	MA	20273
16129	12/31/2021	MD	11758
16900	12/31/2021	ME	1531
17671	12/31/2021	MI	29019
18442	12/31/2021	MN	10516
19213	12/31/2021	MO	16074
19984	12/31/2021	MS	10450
20755	12/31/2021	MT	2906
21526	12/31/2021	NC	19399
22297	12/31/2021	ND	2007
23068	12/31/2021	NE	3341
23839	12/31/2021	NH	1961
24610	12/31/2021	NJ	29037
25381	12/31/2021	NM	5855
26152	12/31/2021	NV	8420
26923	12/31/2021	NY	59289
27694	12/31/2021	OH	29447
28465	12/31/2021	OK	11851
29236	12/31/2021	OR	5655
30007	12/31/2021	PA	36705
30778	12/31/2021	RI	3066
31549	12/31/2021	SC	14636
32320	12/31/2021	SD	2474
33091	12/31/2021	TN	20851
33862	12/31/2021	TX	74778
34633	12/31/2021	UT	3787
35404	12/31/2021	VA	15587
36175	12/31/2021	VT	471
36946	12/31/2021	WA	9853
37717	12/31/2021	WI	11173
38488	12/31/2021	WV	5336
39259	12/31/2021	WY	1526

total_confirmed = total_confirmed_df['Cases'].sum()
print(total_confirmed)

total_death = total_death_df['Deaths'].sum()
print(total_death)

53888739
819360

fig1 = px.bar(total_confirmed_df, x='State', y='Cases', title='Total Confirmed Cases')
fig1.show()

fig1_1 = px.bar(total_death_df, x='State', y='Deaths', title='Total Death Cases')
fig1_1.show()

fig2 = px.bar(total_confirmed_df.sort_values('Cases', ascending=False).head(50),
             x='State', y='Cases', text='Cases', title='Total Confirmed Cases')
fig2.show()

fig2_1 = px.bar(total_death_df.sort_values('Deaths', ascending=False).head(50),
             x='State', y='Deaths', text='Deaths', title='Total Death Cases')
fig2_1.show()

Historical Trends

fig = px.line(confirmed_melt_df, x="Date", y="Cases", color='State', title='Total Confirmed Cases')
fig.show()

fig = px.line(death_melt_df, x="Date", y="Deaths", color='State', title='Total Death Cases')
fig.show()

fig4 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'AK'], x='Date', y='Cases', title='AK Covid Cases')
fig4.show()

fig4_1 = px.line(death_melt_df[death_melt_df['State'] == 'AK'], x='Date', y='Deaths', title='AK Death Cases')
fig4_1.show()

fig5 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'AL'], x='Date', y='Cases', title='AL Covid Cases')
fig5.show()

fig5_1 = px.line(death_melt_df[death_melt_df['State'] == 'AL'], x='Date', y='Deaths', title='AL Death Cases')
fig5_1.show()

fig6 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'AR'], x='Date', y='Cases', title='AR Covid Cases')
fig6.show()

fig6_1 = px.line(death_melt_df[death_melt_df['State'] == 'AR'], x='Date', y='Deaths', title='AR Death Cases')
fig6_1.show()

fig7 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'AZ'], x='Date', y='Cases', title='AZ Covid Cases')
fig7.show()

fig7_1 = px.line(death_melt_df[death_melt_df['State'] == 'AZ'], x='Date', y='Deaths', title='AZ Death Cases')
fig7_1.show()

fig8 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'CA'], x='Date', y='Cases', title='CA Covid Cases')
fig8.show()

fig8_1 = px.line(death_melt_df[death_melt_df['State'] == 'CA'], x='Date', y='Deaths', title='CA Death Cases')
fig8_1.show()

fig9 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'CO'], x='Date', y='Cases', title='CO Confirmed Cases')
fig9.show()

fig9_1 = px.line(death_melt_df[death_melt_df['State'] == 'CO'], x='Date', y='Deaths', title='CO Death Cases')
fig9_1.show()

fig10 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'CT'], x='Date', y='Cases', title='CT Covid Cases')
fig10.show()

fig10_1 = px.line(death_melt_df[death_melt_df['State'] == 'CT'], x='Date', y='Deaths', title='CT Death Cases')
fig10_1.show()

fig11 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'DC'], x='Date', y='Cases', title='DC Covid Cases')
fig11.show()

fig11_1 = px.line(death_melt_df[death_melt_df['State'] == 'DC'], x='Date', y='Deaths', title='DC Death Cases')
fig11_1.show()

fig12 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'DE'], x='Date', y='Cases', title='DE Covid Cases')
fig12.show()

fig12_1 = px.line(death_melt_df[death_melt_df['State'] == 'DE'], x='Date', y='Deaths', title='DE Death Cases')
fig4_1.show()

fig13 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'FL'], x='Date', y='Cases', title='FL Covid Cases')
fig13.show()

fig13_1 = px.line(death_melt_df[death_melt_df['State'] == 'FL'], x='Date', y='Deaths', title='FL Death Cases')
fig13_1.show()

fig14 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'GA'], x='Date', y='Cases', title='GA Covid Cases')
fig14.show()

fig14_1 = px.line(death_melt_df[death_melt_df['State'] == 'GA'], x='Date', y='Deaths', title='GA Death Cases')
fig14_1.show()

fig15 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'HI'], x='Date', y='Cases', title='HI Covid Cases')
fig15.show()

fig15_1 = px.line(death_melt_df[death_melt_df['State'] == 'HI'], x='Date', y='Deaths', title='HI Death Cases')
fig15_1.show()

fig16 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'IA'], x='Date', y='Cases', title='IA Covid Cases')
fig16.show()

fig16_1 = px.line(death_melt_df[death_melt_df['State'] == 'IA'], x='Date', y='Deaths', title='IA Death Cases')
fig16_1.show()

fig17 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'ID'], x='Date', y='Cases', title='ID Covid Cases')
fig17.show()

fig17_1 = px.line(death_melt_df[death_melt_df['State'] == 'ID'], x='Date', y='Deaths', title='ID Death Cases')
fig17_1.show()

fig18 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'IL'], x='Date', y='Cases', title='IL Covid Cases')
fig18.show()

fig18_1 = px.line(death_melt_df[death_melt_df['State'] == 'IL'], x='Date', y='Deaths', title='IL Death Cases')
fig18_1.show()

fig19 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'IN'], x='Date', y='Cases', title='IN Covid Cases')
fig19.show()

fig19_1 = px.line(death_melt_df[death_melt_df['State'] == 'IN'], x='Date', y='Deaths', title='IN Death Cases')
fig19_1.show()

fig20 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'KS'], x='Date', y='Cases', title='KS Covid Cases')
fig20.show()

fig20_1 = px.line(death_melt_df[death_melt_df['State'] == 'KS'], x='Date', y='Deaths', title='KS Death Cases')
fig20_1.show()

fig21 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'KY'], x='Date', y='Cases', title='KY Covid Cases')
fig21.show()

fig21_1 = px.line(death_melt_df[death_melt_df['State'] == 'KY'], x='Date', y='Deaths', title='KY Death Cases')
fig21_1.show()

fig22 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'LA'], x='Date', y='Cases', title='LA Covid Cases')
fig22.show()

fig22_1 = px.line(death_melt_df[death_melt_df['State'] == 'LA'], x='Date', y='Deaths', title='LA Death Cases')
fig22_1.show()

fig23 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MA'], x='Date', y='Cases', title='MA Covid Cases')
fig23.show()

fig23_1 = px.line(death_melt_df[death_melt_df['State'] == 'MA'], x='Date', y='Deaths', title='MA Death Cases')
fig23_1.show()

fig24 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MD'], x='Date', y='Cases', title='MD Covid Cases')
fig24.show()

fig24_1 = px.line(death_melt_df[death_melt_df['State'] == 'MD'], x='Date', y='Deaths', title='MD Death Cases')
fig24_1.show()

fig25 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'ME'], x='Date', y='Cases', title='ME Covid Cases')
fig25.show()

fig25_1 = px.line(death_melt_df[death_melt_df['State'] == 'ME'], x='Date', y='Deaths', title='ME Death Cases')
fig25_1.show()

fig26 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MI'], x='Date', y='Cases', title='MI Covid Cases')
fig26.show()

fig26_1 = px.line(death_melt_df[death_melt_df['State'] == 'MI'], x='Date', y='Deaths', title='MI Death Cases')
fig26_1.show()

fig27 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MN'], x='Date', y='Cases', title='MN Covid Cases')
fig27.show()

fig27_1 = px.line(death_melt_df[death_melt_df['State'] == 'MN'], x='Date', y='Deaths', title='MN Death Cases')
fig27_1.show()

fig28 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MO'], x='Date', y='Cases', title='MO Covid Cases')
fig28.show()

fig28_1 = px.line(death_melt_df[death_melt_df['State'] == 'MO'], x='Date', y='Deaths', title='MO Death Cases')
fig28_1.show()

fig29 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MS'], x='Date', y='Cases', title='MS Covid Cases')
fig29.show()

fig29_1 = px.line(death_melt_df[death_melt_df['State'] == 'MS'], x='Date', y='Deaths', title='MS Death Cases')
fig29_1.show()

fig30 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'MT'], x='Date', y='Cases', title='MT Covid Cases')
fig30.show()

fig30_1 = px.line(death_melt_df[death_melt_df['State'] == 'MT'], x='Date', y='Deaths', title='MT Death Cases')
fig30_1.show()

fig31 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NC'], x='Date', y='Cases', title='NC Covid Cases')
fig31.show()

fig31_1 = px.line(death_melt_df[death_melt_df['State'] == 'NC'], x='Date', y='Deaths', title='NC Death Cases')
fig31_1.show()

fig32 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'ND'], x='Date', y='Cases', title='ND Covid Cases')
fig32.show()

fig32_1 = px.line(death_melt_df[death_melt_df['State'] == 'ND'], x='Date', y='Deaths', title='ND Death Cases')
fig32_1.show()

fig33 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NE'], x='Date', y='Cases', title='NE Covid Cases')
fig33.show()

fig33_1 = px.line(death_melt_df[death_melt_df['State'] == 'NE'], x='Date', y='Deaths', title='NE Death Cases')
fig33_1.show()

fig34 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NH'], x='Date', y='Cases', title='NH Covid Cases')
fig34.show()

fig34_1 = px.line(death_melt_df[death_melt_df['State'] == 'NH'], x='Date', y='Deaths', title='NH Death Cases')
fig34_1.show()

fig35 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NJ'], x='Date', y='Cases', title='NJ Covid Cases')
fig35.show()

fig35_1 = px.line(death_melt_df[death_melt_df['State'] == 'NJ'], x='Date', y='Deaths', title='NJ Death Cases')
fig35_1.show()

fig36 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NM'], x='Date', y='Cases', title='NM Covid Cases')
fig36.show()

fig36_1 = px.line(death_melt_df[death_melt_df['State'] == 'NM'], x='Date', y='Deaths', title='NM Death Cases')
fig36_1.show()

fig37 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NV'], x='Date', y='Cases', title='NV Covid Cases')
fig37.show()

fig37_1 = px.line(death_melt_df[death_melt_df['State'] == 'NV'], x='Date', y='Deaths', title='NV Death Cases')
fig37_1.show()

fig38 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'NY'], x='Date', y='Cases', title='NY Covid Cases')
fig38.show()

fig38_1 = px.line(death_melt_df[death_melt_df['State'] == 'NY'], x='Date', y='Deaths', title='NY Death Cases')
fig38_1.show()

fig39 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'OH'], x='Date', y='Cases', title='OH Covid Cases')
fig39.show()

fig39_1 = px.line(death_melt_df[death_melt_df['State'] == 'OH'], x='Date', y='Deaths', title='OH Death Cases')
fig39_1.show()

fig40 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'OK'], x='Date', y='Cases', title='OK Covid Cases')
fig40.show()

fig40_1 = px.line(death_melt_df[death_melt_df['State'] == 'OK'], x='Date', y='Deaths', title='OK Death Cases')
fig40_1.show()

fig41 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'OR'], x='Date', y='Cases', title='OR Covid Cases')
fig41.show()

fig41_1 = px.line(death_melt_df[death_melt_df['State'] == 'OR'], x='Date', y='Deaths', title='OR Death Cases')
fig41_1.show()

fig42 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'PA'], x='Date', y='Cases', title='PA Covid Cases')
fig42.show()

fig42_1 = px.line(death_melt_df[death_melt_df['State'] == 'PA'], x='Date', y='Deaths', title='PA Death Cases')
fig42_1.show()

fig43 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'RI'], x='Date', y='Cases', title='RI Covid Cases')
fig43.show()

fig43_1 = px.line(death_melt_df[death_melt_df['State'] == 'RI'], x='Date', y='Deaths', title='RI Death Cases')
fig43_1.show()

fig44 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'SC'], x='Date', y='Cases', title='SC Covid Cases')
fig44.show()

fig44_1 = px.line(death_melt_df[death_melt_df['State'] == 'SC'], x='Date', y='Deaths', title='SC Death Cases')
fig44_1.show()

fig45 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'SD'], x='Date', y='Cases', title='SD Covid Cases')
fig45.show()

fig45_1 = px.line(death_melt_df[death_melt_df['State'] == 'SD'], x='Date', y='Deaths', title='SD Death Cases')
fig45_1.show()

fig46 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'TN'], x='Date', y='Cases', title='TN Covid Cases')
fig46.show()

fig46_1 = px.line(death_melt_df[death_melt_df['State'] == 'TN'], x='Date', y='Deaths', title='TN Death Cases')
fig46_1.show()

fig47 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'TX'], x='Date', y='Cases', title='TX Covid Cases')
fig47.show()

fig47_1 = px.line(death_melt_df[death_melt_df['State'] == 'TX'], x='Date', y='Deaths', title='TX Death Cases')
fig47_1.show()

fig48 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'UT'], x='Date', y='Cases', title='UT Covid Cases')
fig48.show()

fig48_1 = px.line(death_melt_df[death_melt_df['State'] == 'UT'], x='Date', y='Deaths', title='UT Death Cases')
fig48_1.show()

fig49 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'VA'], x='Date', y='Cases', title='VA Covid Cases')
fig48.show()

fig49_1 = px.line(death_melt_df[death_melt_df['State'] == 'VA'], x='Date', y='Deaths', title='VA Death Cases')
fig49_1.show()

fig50 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'VT'], x='Date', y='Cases', title='VT Covid Cases')
fig50.show()

fig50_1 = px.line(death_melt_df[death_melt_df['State'] == 'VT'], x='Date', y='Deaths', title='VT Death Cases')
fig50_1.show()

fig51 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'WA'], x='Date', y='Cases', title='WA Covid Cases')
fig51.show()

fig51_1 = px.line(death_melt_df[death_melt_df['State'] == 'WA'], x='Date', y='Deaths', title='WA Death Cases')
fig51_1.show()

fig52 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'WI'], x='Date', y='Cases', title='WI Covid Cases')
fig52.show()

fig52_1 = px.line(death_melt_df[death_melt_df['State'] == 'WI'], x='Date', y='Deaths', title='WI Death Cases')
fig52_1.show()

fig53 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'WV'], x='Date', y='Cases', title='WV Covid Cases')
fig53.show()

fig53_1 = px.line(death_melt_df[death_melt_df['State'] == 'WV'], x='Date', y='Deaths', title='WV Death Cases')
fig53_1.show()

fig54 = px.line(confirmed_melt_df[confirmed_melt_df['State'] == 'WY'], x='Date', y='Cases', title='WY Covid Cases')
fig54.show()

fig54_1 = px.line(death_melt_df[death_melt_df['State'] == 'WY'], x='Date', y='Deaths', title='WY Death Cases')
fig54_1.show()

Group daily cases to monthly

confirmed_melt_df['Date'] = pd.to_datetime(confirmed_melt_df['Date'], errors='coerce')
death_melt_df['Date'] = pd.to_datetime(death_melt_df['Date'], errors='coerce')

c_df = (confirmed_melt_df.groupby([pd.Grouper(key='Date', freq='MS'), 'State'])['Cases']
   .sum()
   .reset_index())
d_df = (death_melt_df.groupby([pd.Grouper(key='Date', freq='MS'), 'State'])['Deaths']
   .sum()
   .reset_index())

c_df.head()

	Date	State	Cases
0	2020-01-01	AK	0
1	2020-01-01	AL	0
2	2020-01-01	AR	0
3	2020-01-01	AZ	6
4	2020-01-01	CA	7594

d_df.head()

	Date	State	Deaths
0	2020-01-01	AK	0
1	2020-01-01	AL	0
2	2020-01-01	AR	0
3	2020-01-01	AZ	0
4	2020-01-01	CA	0

#Create Month and Year Column
c_df['month'] = c_df['Date'].dt.month
d_df['month'] = d_df['Date'].dt.month
c_df['year'] = c_df['Date'].dt.year
d_df['year'] = d_df['Date'].dt.year

c_df

	Date	State	Cases	month	year
0	2020-01-01	AK	0	1	2020
1	2020-01-01	AL	0	1	2020
2	2020-01-01	AR	0	1	2020
3	2020-01-01	AZ	6	1	2020
4	2020-01-01	CA	7594	1	2020
...	...	...	...	...	...
1372	2022-03-01	VT	225456	3	2022
1373	2022-03-01	WA	2851003	3	2022
1374	2022-03-01	WI	3146145	3	2022
1375	2022-03-01	WV	981586	3	2022
1376	2022-03-01	WY	309818	3	2022

1377 rows × 5 columns

#Rename Month and Year Rows
c_df['month'] = c_df['month'].replace({1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'})
d_df['month'] = d_df['month'].replace({1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'})

c_df.head()

	Date	State	Cases	month	year
0	2020-01-01	AK	0	Jan	2020
1	2020-01-01	AL	0	Jan	2020
2	2020-01-01	AR	0	Jan	2020
3	2020-01-01	AZ	6	Jan	2020
4	2020-01-01	CA	7594	Jan	2020

#early = c_df.loc[c_df['State'].isin(['AL','AK','AZ','AR','FL','GA','ID','IN','IA','MD','MS','MO','NE','NH','ND','OH','OK','SC','TN','TX','UT','WV','WY'])

#Select Rows
c_early = c_df[c_df['State'].isin(['AL','AK','AZ','AR','FL','GA','ID','IN','IA','MD','MS','MO','NE','NH','ND','OH','OK','SC','TN','TX','UT','WV','WY']) ]

c_early

	Date	State	Cases	month	year
0	2020-01-01	AK	0	Jan	2020
1	2020-01-01	AL	0	Jan	2020
2	2020-01-01	AR	0	Jan	2020
3	2020-01-01	AZ	6	Jan	2020
9	2020-01-01	FL	0	Jan	2020
...	...	...	...	...	...
1368	2022-03-01	TN	3990080	Mar	2022
1369	2022-03-01	TX	13111654	Mar	2022
1370	2022-03-01	UT	1846721	Mar	2022
1375	2022-03-01	WV	981586	Mar	2022
1376	2022-03-01	WY	309818	Mar	2022

621 rows × 5 columns

#Select Rows
d_early = d_df[d_df['State'].isin(['AL','AK','AZ','AR','FL','GA','ID','IN','IA','MD','MS','MO','NE','NH','ND','OH','OK','SC','TN','TX','UT','WV','WY']) ]

d_early

	Date	State	Deaths	month	year
0	2020-01-01	AK	0	Jan	2020
1	2020-01-01	AL	0	Jan	2020
2	2020-01-01	AR	0	Jan	2020
3	2020-01-01	AZ	0	Jan	2020
9	2020-01-01	FL	0	Jan	2020
...	...	...	...	...	...
1368	2022-03-01	TN	47962	Mar	2022
1369	2022-03-01	TX	167798	Mar	2022
1370	2022-03-01	UT	8853	Mar	2022
1375	2022-03-01	WV	12720	Mar	2022
1376	2022-03-01	WY	3482	Mar	2022

621 rows × 5 columns

#Select specific months
c_early_df = c_early[c_early['month'].isin(['Jun','Jul','Aug','Sep','Oct']) ]
d_early_df = d_early[d_early['month'].isin(['Jun','Jul','Aug','Sep','Oct']) ]

c_early_df

	Date	State	Cases	month	year
255	2020-06-01	AK	21232	Jun	2020
256	2020-06-01	AL	795489	Jun	2020
257	2020-06-01	AR	402084	Jun	2020
258	2020-06-01	AZ	1292313	Jun	2020
264	2020-06-01	FL	2637713	Jun	2020
...	...	...	...	...	...
1113	2021-10-01	TN	39069931	Oct	2021
1114	2021-10-01	TX	128680099	Oct	2021
1115	2021-10-01	UT	16424825	Oct	2021
1120	2021-10-01	WV	7992616	Oct	2021
1121	2021-10-01	WY	3014173	Oct	2021

230 rows × 5 columns

d_early_df

	Date	State	Deaths	month	year
255	2020-06-01	AK	354	Jun	2020
256	2020-06-01	AL	23622	Jun	2020
257	2020-06-01	AR	5861	Jun	2020
258	2020-06-01	AZ	37242	Jun	2020
264	2020-06-01	FL	89621	Jun	2020
...	...	...	...	...	...
1113	2021-10-01	TN	490703	Oct	2021
1114	2021-10-01	TX	2120540	Oct	2021
1115	2021-10-01	UT	95057	Oct	2021
1120	2021-10-01	WV	126519	Oct	2021
1121	2021-10-01	WY	33892	Oct	2021

230 rows × 5 columns

c_early_df1 = c_early_df[c_early_df['year'].isin([2021]) ]
d_early_df1 = d_early_df[d_early_df['year'].isin([2021]) ]

fig = px.line(c_early_df1, x="month", y="Cases", color='State', title='Total Confirmed Cases')
fig.show()

fig = px.line(d_early_df1, x="month", y="Deaths", color='State', title='Total Death Cases')
fig.show()

plot_daywise_line('Deaths / 100 Cases', dth)